Automatic Detection of InSAR Deformation Signals Using a Realistic Tropospheric Turbulence Noise Model

As the growth of interferometric synthetic aperture radar (In-SAR) data continues to accelerate, there is an increasing need to detect surface deformation features from gigantic datasets in an automated fashion. We have developed a computer vision algorithm for automatic detection of InSAR deformation signals using multi-scale Laplacian-of-Gaussian (LoG) filters. We applied the algorithm to InSAR-derived land surface deformation maps in West Texas, where there has been a rise in the level of seismic activity near areas of hydrocarbon production. Our algorithm detected numerous surface deformation features without the need for manual inspection of signals. To control the false detection rate, here we employed a realistic tropospheric turbulence noise model and computed an empirical probability density for the magnitudes of candidate detections under the null hypothesis. We showed that the likelihoods from this PDF can be used to set an acceptable false detection rate for near real-time deformation monitoring applications.